Effluent-dominated streams. Part 2: Presence and possible effects of pharmaceuticals and personal care products in Wascana Creek, Saskatchewan, Canada
Bibliographic record
Abstract
Recent worldwide surveys have not only established incomplete removal of pharmaceuticals and personal care products (PPCPs) by sewage treatment plants, but also their presence in surface waters receiving treated sewage effluent. Those aquatic systems where sewage effluent dominates flow are thought to be at the highest risk for ecosystem level changes. The city of Regina, Saskatchewan, Canada (population 190,400) treats its sewage at a modern tertiary sewage treatment facility located on Wascana Creek. The Wascana Creek hydrograph is dominated by one major event: spring snow melt. Thereafter, creek flow declines considerably and in winter treated sewage effluent makes up almost 100% of stream flow. Four water surveys conducted on the creek from winter 2005 to spring 2007 indicated that PPCPs were always present, in nanogram and sometimes microgram per liter concentrations downstream of the sewage treatment plant. This mixture included antibiotics, analgesics, antiinflammatories, a lipid regulator, metabolites of caffeine, cocaine and nicotine, and an insect repellent. Not surprisingly, concentrations of some PPCPs were highest in winter. According to hazard quotient calculations and homologue presence, ibuprofen, naproxen, gemfibrozil, triclosan, erythromycin, trimethoprim, and sulfamethoxazole were present in Wascana Creek at concentrations that may present a risk to aquatic organisms. The continual exposure to a mixture of pharmaceuticals as well as concentrations of un-ionized ammonia that far exceed Canadian and American water quality guidelines suggests that Wascana Creek should be considered an ecosystem at risk. Although the Wascana Creek study is regional in nature, the results highlight the considerable risks posed to aquatic organisms in such effluent-dominated ecosystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".